Optimized Operation Model of Urban Energy Internet with Multi-Category Energy Subsystems via Model Predictive Control

Upon connecting numerous renewable power sources to the power grid, the inherent high randomness and volatility in renewable energy often result in challenges such as unbalanced power supply, diminished power quality, and unstable operational statuses. These challenges significantly elevate the complexity of urban energy operation and management. Moreover, with each micro energy network system catering to different energy preferences, conventional management practices within a single energy category must evolve to address multi-category energy optimization and control. This paper proposes an optimization and control framework for the urban energy internet, encompassing multi-category energy subsystems. Firstly, it formulates an optimization model for the urban energy internet aimed at maximizing integrated value across various energy categories. Secondly, it employs a model predictive control algorithm that relies on a two-time prediction domain for continuous optimization. Validation of the proposed optimization method is conducted using the modified IEEE 30-bus system distributed micro energy network. The results demonstrate that the proposed method optimizes the micro energy network with respect to their preferences.